• Proceedings of the Technology Innovation Conference
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• 1997.07a
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• pp.160-171
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• 1997

This paper starts out by reviewing the literature that in different ways utilizes patent data as a output of R&D investment. The main focus, however, is an analysis of time-lag between R&D input and output. To achieve this research objective, the basic data associated with the R&D input(expenditure, researchers) and output(patent, utilities) for the past 15 years, from 1980 to 1994, in the areas of electrical-electronic, mechanical and chemical industries have been collected. And the raw output data were altered it to objective data using Laspeyres approach and analyzed using multiple regression analysis, especially stepwise regression analysis. The result of this study can be summarized as follows: a) The time-lag between R&D input and output is from 1 to 4 years. This result is equal to the research conclusion of the existing foreign studies. b) It was found that the time-lag of patents was longer than of utility models. c) It was showed that the time-lag of electrical-electronic, mechanical industry was longer than the chemical one.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.63 no.6
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• pp.797-803
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• 2014

In this paper, 3D face recognition system is designed by using polynomial based on RBFNNs. In case of 2D face recognition, the recognition performance reduced by the external environmental factors such as illumination and facial pose. In order to compensate for these shortcomings of 2D face recognition, 3D face recognition. In the preprocessing part, according to the change of each position angle the obtained 3D face image shapes are changed into front image shapes through pose compensation. the depth data of face image shape by using Multiple Point Signature is extracted. Overall face depth information is obtained by using two or more reference points. The direct use of the extracted data an high-dimensional data leads to the deterioration of learning speed as well as recognition performance. We exploit principle component analysis(PCA) algorithm to conduct the dimension reduction of high-dimensional data. Parameter optimization is carried out with the aid of PSO for effective training and recognition. The proposed pattern classifier is experimented with and evaluated by using dataset obtained in IC & CI Lab.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.11
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• pp.1905-1909
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• 2008

As catenary supply electric power directly to the railway system, it is very important to prevent an accident of a catenary for appropriate train operations. This paper analyzed the outage data for British catenary safety analysis report and Korean data to compare the reliability of the railway systems. The analyzed data were applied to Fault Tree Analysis(FTA) algorithm to calculate the reliability indices of a railway system. Failure rate of an electric railway system through FTA were calculated for each element and the entire railway system. The reliability indices can be used to determine the eqipment to be replaced for the entire system reliability improvement.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.66 no.4
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• pp.258-262
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• 2017

The PCA(Principal Component Analysis) algorithm is widely used as a technique of expressing the eigenvectors of the covariance matrix that best represents the characteristics of the data and reducing the high dimensional vector to a low dimensional vector. In this paper, we have developed a personal authentication method based on ECG using principal component analysis. The proposed method showed excellent recognition performance of 98.2 [%] when it was experimented using electrocardiogram data obtained at weekly intervals. Therefore, it can be seen that it is useful for personal authentication by reducing the dimension without changing the information on the variability and the correlation set variable existing in the electrocardiogram data by using the principal component analysis technique.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.25 no.12
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• pp.54-59
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• 2011

LED lightings typically do not fail catastrophically during use. However, over time the light output will gradually depreciate. Even if there are same LED lighting, they are so different at all. because of dissimilar the use and environment of each LED lighting. In this paper, we make a description of reliability analysis procedures for the degradation data using collected field data. Reliability analysis procedures are consisted of estimating degradation models and failure time, verification of distribution and parameters of the distribution, and reliability measures estimation. At some point in time, the light emitted from an LED lightings depreciates to a level where it is no longer considered adequate for a specific application.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.11
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• pp.498-504
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• 2006

This paper describes the simple payback period analysis of small co-generation system based on the existing apartment and building data. First, We investigate apartment and building data more than $2000[m^2]$ using Ministry of Construction & Transportation's computer system. And then we calculate the latent amount of small co-generation system considering gas company and CHP. Second, we classify the latent amount of small co-generation system into office, hospital, hotel, department store, complex building and apartment. Finally, we perform the simple payback period analysis for small co-generation system. The results show the simple payback period of small co-generation system is less then 10 years.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1869-1873
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• 2010

For reliable operation of oil-filled electrical equipment, monitoring and maintenance of insulating oil is essential. Dissolved gas analysis(DGA) is widely used for monitoring faults in high voltage electrical equipment in service. Therefore, oil analysis should be monitored regularly during its service life. KEPCO has investigated thousands of dissolved gas analysis data since 1985, and conducted studies on the relationship of gas in oil analysis and internal inspection results of transformer. As the results, KEPCO revised criteria for transformer diagnosis and has applied it since 2008. Almost of 100 cases of internal inspection results since 2001 have been investigated. This paper presents the correlation of the fault-identifying gases with faults found in actual transformers and how should we approach to internal inspection of transformer by dissolved gas analysis.

• Journal of Electrical Engineering and Technology
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• v.9 no.1
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• pp.114-120
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• 2014

In this paper, a method for multi-physics analysis of the temperature-dependent properties of an oil-immersed transformer is discussed. To couple thermal fields with electromagnetic and fluid fields, an algorithm employing a user defined function (UDF) is proposed. Using electromagnetic analysis, electric power loss dependent on temperature rise is calculated; these are used as input data for multi-physics analysis in order to predict the temperature rise. A heat transfer coefficient is applied only at the outermost boundary between transformer and the atmosphere in order to reduce the analysis region. To verify the validity of the proposed method, the predicted temperature rises in high-voltage (HV) and low-voltage (LV) windings and radiators were compared with the experimental values.

• Journal of Electrical Engineering and Technology
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• v.13 no.6
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• pp.2511-2520
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• 2018

In this study, Multi-Radial Basis Function Support Vector Machine (Multi-RBF SVM) classifier is introduced based on a composite kernel function. In the proposed multi-RBF support vector machine classifier, the input space is divided into several local subsets considered for extremely nonlinear classification tasks. Each local subset is expressed as nonlinear classification subspace and mapped into feature space by using kernel function. The composite kernel function employs the dual RBF structure. By capturing the nonlinear distribution knowledge of local subsets, the training data is mapped into higher feature space, then Multi-SVM classifier is realized by using the composite kernel function through optimization procedure similar to conventional SVM classifier. The original training data set is partitioned by using some unsupervised learning methods such as clustering methods. In this study, three types of clustering method are considered such as Affinity propagation (AP), Hard C-Mean (HCM) and Iterative Self-Organizing Data Analysis Technique Algorithm (ISODATA). Experimental results on benchmark machine learning datasets show that the proposed method improves the classification performance efficiently.

• Journal of Electrical Engineering and Technology
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• v.9 no.6
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• pp.1812-1821
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• 2014

Wind has been a rapidly growing renewable power source for the last twenty years. Since wind behavior is chaotic in nature, its forecasting is not easy. At the same time, developing an accurate forecasting method is essential when wind farms are integrated into the power grid. In fact, wind speed forecasting tools can solve issues related to grid stability and reserve allocation. In this paper 30 hours ahead wind speed profile forecast is proposed using Adaptive Wavelet Neural Network (AWNN). The implemented AWNN uses a Mexican hat mother Wavelet, and Morlet Mother Wavelet for seven, eight and nine levels decompositions. For wind speed forecasting, the time series data on wind speed has been gathered from the National Renewable Energy Laboratory (NREL) website. In this work, hourly averaged 10-min wind speed data sets for the year 2004 in the Midwest ISO region (site number 7263) is taken for analysis. Data sets are normalized in the range of [-1, 1] to improve the training performance of forecasting models. Total 8760 samples were taken for this forecasting analysis. After the forecasting phase, statistical parameters are calculated to evaluate system accuracy, comparing different configurations.